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Radio Resource Management for Green 3GPP Long T
Evolution Cellular Networks: Review and Trade-offsMustafa Ismael
Salman
a, Muntadher Qasim Abdulhasan
a, Chee Kyun Ng
a, Nor Kamariah
Noordina, Aduwati Sali
a& Borhanuddin Mohd Ali
a
aDepartment of Computer and Communication Systems Engineering,
Faculty of Engine
Universiti Putra Malaysia, UPM Serdang, 43400, Selangor Darul
Ehsan, Malaysia
Published online: 01 Sep 2014.
To cite this article:Mustafa Ismael Salman, Muntadher Qasim
Abdulhasan, Chee Kyun Ng, Nor Kamariah Noordin, Aduwa&
Borhanuddin Mohd Ali (2013) Radio Resource Management for Green
3GPP Long Term Evolution Cellular Networks: Revand Trade-offs, IETE
Technical Review, 30:3, 257-269
To link to this article:
http://dx.doi.org/10.4103/0256-4602.113526
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257IETE TECHNICAL REVIEW | VOL 30 | ISSUE 3 | MAY-JUN 2013
Radio Resource Management for Green 3GPP Long Term
Evolution Cellular Networks: Review and Trade-offs
Mustafa Ismael Salman, Muntadher Qasim Abdulhasan, Chee Kyun Ng,
Nor Kamariah Noordin,
Aduwati Sali and Borhanuddin Mohd Ali
Department of Computer and Communication Systems Engineering,
Faculty of Engineering, Universiti Putra Malaysia,UPM Serdang,
43400, Selangor Darul Ehsan, Malaysia
Abstract
Conventional design of cellular systems aims to maximize the
system capacity and spectral efficiency dueto sustainable growth of
data rate requirements. As the energy consumption becomes
relatively high, ener-
gy-efficient design for cellular systems is highly required to
save energy as well as reducing the undesir-able carbon dioxide
emitted by these systems. However, reducing the energy consumption
will degrade
other system performances such as the data rate and quality of
service. Therefore, joint optimization foroverall system
performances should be achieved. In this paper, the
energy-efficient radio resource man-
agement (RRM) for Long Term Evolution (LTE) systems is
addressed. After a brief introduction to LTE radioresource block
and LTE frame, different types of energy efficiency metrics are
defined to give a better under-
standing to the energy efficiency perspectives. The
energy-efficient approaches related to link adaptationand RRM are
explained. The state-of-the-art energy-efficient schedulers are
also discussed, and a compre-hensive comparison between them is
adopted in this paper. Moreover, many trade-offs, challenges,
and
open issues are addressed to optimize the system
performances.
Keywords
Adaptive modulation and coding, Energy efficiency, Green
communication, Link adaptation, Multi-input-multi-
output, Orthogonal frequency division multiple access, Resource
allocation, 3GPP long term evolution.
On the other hand, the continuous growth in wirelessdata trafc
results in the increase of energy consumed by
wireless networks, which leads to undesirable increasein carbon
dioxide (CO2) emission. For example, the
total energy consumed by a network of 20,000 3G basestations is
about 58 MW, resulting in an annual cost of$62 million and a carbon
footprint of 11 tons for eachcell site [3]. The CO
2emission is considered as the chief
greenhouse gas that resulted from wireless networksand other
human activities, and causes the globalwarming and climate changes.
Stephen Ruth in [4]has investigated several leading approaches that
havebeen used to reduce the CO
2 emitted by information
and communications technology. Although there areserious efforts
to reduce the amount of CO
2 emission
per mobile subscriber, as shown in Figure 1 [5], cleanerand
efcient solutions for wireless communications isurgently
required.
The cellular network power consumption can be classi-ed into ve
categories as shown in Figure 2 [6]. Thesecategories give us an
insight into the possible researchavenues for reducing energy
consumption in cellularnetwork. It is obviously noticed that the
major amountof the cellular network power is consumed by the
basestations. However, the power consumed by transmission
1. Introduction
Escalation of wireless and cellular systems continuesto stir up
new research avenues that enable these sys-tems to meet the growing
demands and to work undervarious limitations. Green Radio
Technology [1,2]is among areas which have been adopted recentlyto
overcome the limitations in the radio spectrum aswell as reducing
the energy consumed by the wirelesssystems.
The limitation in radio spectrum comes from the fact thatthe
spectrum is xed and it is not free. The more wirelessapplications
and technologies used, the more bandwidthrequired. Wireless data
trafc has increased in recent
years due to the variety of applications and smart soft-ware and
devices. It has also increased due to the pres-ence of many social
networking applications through theinternet, such as Facebook and
Twitter. Moreover, it hasbeen expected that this growth will
continue increasingexponentially, especially with the exploitation
of the3GPP Long Term Evolution (LTE)-Advanced cellularnetworks,
which should support up to 1 Gbps in thedownlink transmission.
Therefore, the radio spectrumresources should be utilized as
efciently as possible toovercome the bandwidth limitations.
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process is also momentous due to the sustainable growthof data
rate requirements. Therefore, in this paper, wewill address the
energyefcient approaches that canreduce the energy consumption in
the core transmis-sion. Unlike most of the review articles
available in theliterature related to green communications [6,7],
thispaper discusses exclusively the energyefcient link
adaptation and resource scheduling techniques relatedto 3GPP LTE
systems.
In energy-efficient link adaptation, we carry out adetailed
survey on adaptive modulation and cod-ing (AMC), power control and
multi-input-multi-out-put (MIMO) antenna, and highlight the
research oppor-tunities that make these techniques green.
Althoughthe link adaptation needs more control signaling, it
isshown that adapting some or all of these link propertieswill help
the system to maximize its energy efciency.Moreover, link
adaptation alongside with the gain ofmultiuser diversity will give
the cellular systems more
exibility to make a proper decision in allocating the
radio resources among users. Therefore, energyefcientradio
resource management (RRM) is also discussed inthis paper. A
comparison between the state-of-the-artenergyefcient resource
schedulers is carried out. Fur-thermore, many types of trade-offs
between the energyefciency, spectral efciency, fairness, and delay
areinvestigated to meet the 3GPP LTE requirements.
The rest of the paper is organized as follows. In Section 2,a
brief overview to LTE radio resource block (RB) andframe structure
is introduced. Then, the energy efciencywith the related radio
transmission metrics is dened inSection 3. In Section 4, a review
of the energyefcientcellular transmission by using energy-efficient
linkadaptation strategies is provided. Section 5 will covervarious
energyefcient resource allocation proceduresand algorithms proposed
in the literature. Finally,conclusions and recommendations for
future work arediscussed in Section 6.
2. LTE Radio Resource Management
RRM is essential for LTE cellular networks because ofthe
scarceness of radio resources which should be sharedby multiple
users. The RRM involves many strategies toutilize the limited power
and bandwidth resources in anefcient way whereby a reliable
transmission is satis-ed. Furthermore, the radio resources can be
managedto achieve a spectralefcient transmission with
highthroughput and low latency, which is highly requiredfor LTE
networks. The RRM is usually categorizedinto two parts, scheduling
and resource allocation. Thescheduler normally decides which user
to be servedand determines the number of packets that should
bescheduled in the current frame. However, the resourceallocator
decides which RB is assigned to the selecteduser, and determines
the number of RBs required forsatisfying the user requirements. The
resource alloca-tor assigns RBs to user with the best channel
conditionand/or according to their QoS requirements in order
toimprove the overall system performances. RB is a blockof 12
subcarriers in the frequency domain and 7 (or 6)symbols in time
domain. Hence, there is a grid of 84resource elements per RB, each
can be represented by 2,4, or 6 bits depending on the type of used
modulation
as shown in Figure 3 [8]. In the frequency domain, theLTE
transmission bandwidth can be chosen between 1.4to 20 MHz due to
the non-utilized spectrum, and thus,there will be different numbers
of RBs to be allocatedto the users according to the used channel
bandwidthas shown in Table 1. In the time domain, the 10 ms LTE
Table 1: LTE channel bandwidth
Channel bandwidth [MHz] 1.4 3 5 10 15 20
Transmission bandwidth [MHz] 1.08 2.7 4.5 9 13.5 18Figure 2:
Power consumption of a typical wireless cellular
network.
Figure 1:The amount of CO2emitted per subscriber [5].
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frame is divided into 10 subframes with each consisting
of two 0.5 ms time slots. The 0.5 ms time slot representsthe
time duration of each RB. Further information onLTE frame structure
can be found on [8].
3. Energy Efciency Metrics
In general, The more energyefcient the communica-tion system is,
the less energy it needs to achieve the sametask [9]. However,
energy efciency can be dened indifferent ways according to the
purpose of designedsystem. And accordingly, the energy efciency
metricscan be addressed. Rather than the denition, the
energyefciency metrics should reect how green the wireless
system is. Therefore, the energy efciency metrics canbe classied
into three categories [10], componentlevel,equipment-level, and
system-level metrics.
The component-level metrics include low-level energyefciency
rating for individual parts inside the wire-less equipments;
antenna system, baseband processor,etc. In the equipment level,
metrics should reect theenergy efciency of whole base station or
wireless accesspoint [11]. Finally, system (network) level metrics
wouldconsider the energy efciency for the entire network. Thislevel
metrics can be classied according to the classesof wireless network
such as cellular, wireless local area
network, ad-hoc, and satellite networks. In this paper,we will
discuss some of these metrics which are relatedto data transmission
of LTE cellular systems.
3.1 Transmission Energy Efciency
The number of bits transmitted per joule of energyreflects how
energy-efficient the transmission linkbetween the base station and
the user equipment is. Thismetric represents the transmission rate
energy efciencywhich is given by [12]
U R R
P R
R
P Pt c
( )( )
,= =+ (1)
where, Pc is the circuit power consumption, P
t is the
transmit power, and Ris the achievable data rate.
3.2 Energy Consumption Rate
Energy consumption rate (ECR) is a framework formeasuring the
energy efciency of network and tele-com devices [13]. This metric
is considered as a validdifferentiator between the networking and
telecomequipments. For example, equipment with lower ECRconsumes
less energy to drive the same amount of
payload. The ECR can be dened as the consumedenergy divided by
the effective full-duplex throughputas given by
ECR E
T= , (2)
where, Erepresents the energy consumption in watts,andTdenotes
the effective system throughput in bits persecond. In LTE systems,
it is highly required to determinethe ECR corresponding to various
base station equip-ments. The basic power consumption models of
differentbase stations have been discussed in details in [14].
Asshown in Figure 4, the basic equipments in base station
are rectier, power amplier, baseband signal processingunit,
feeder, antenna, and cooling systems. Moreover,the base station
site may also incorporate other supportsand/or supplementary
cabinets that are not includedwith the base station main
equipments, and it shouldbe considered in the calculation of the
ECR. Each ofthese equipments has different activity levels of
powerconsumptions due to different load conditions, i.e. inLTE
cellular networks, three activity levels are denedcorresponding to
the busy hour, medium term load, andlow load [15].
Figure 3: The LTE downlink physical resource based on OFDM.
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3.3 Energy Reduction Gain
In order to compare between the performances of twosystems, a
useful metric called Energy ConsumptionGain (ECG) can be used to
show difference in energyconsumption between the baseline and the
new cellularsystem. In contrast, the energy reduction gain (ERG)
[6]can be used to show the percentage in saving energygain between
two systems, and it can be calculated as
ERGECG
= 1 1
, (3)
ERG can determine the saving in energy consumptionwhen there are
two different systems having to deliverthe same amount of data
through the same duration oftime. However, such metrics give
relative measurements,and therefore, the energy-related
calculations were notdone in a fair manner.
3.4 Telecommunications Equipment Energy
Efciency Rating
Telecommunications equipment energy efciency rat-ing (TEEER) is
an equipment-level metric that calculatesthe energy efciency of
individual pieces of cellular net-work equipments at various
utilization levels. TEEER isproposed by Alliance for
telecommunications industrysolutions [16] to calculate the energy
efciency rating forspecic products. Prior to calculate TEEER, the
equip-ment under test (EUT) should be examined under threelevels of
utilizations which are 100%, 50%, and 25% forfull-load, medium, and
low utilizations, respectively.At each level, a corresponding
required power should
be provided to the EUT over a period of 15 minutes forstability
purposes, and the value is recorded. Then, thetotal power
consumption for this EUT can be representedby a weighting formula
as
P P P Ptotal sleep= + + ( . ) ( . ) ( . ),max0 35 0 4 0 2550
(4)
where,Pmax
, P50, andP
sleepare the measured input powers
with the EUT while operating at maximum load, 50%of maximum
load, and no activity mode, respectively.However, the weighting
values may not be the samefor all operators. According to the
calculated value ofP
total above, the TEEER can be calculated for different
equipments according to the formulas shown inTable 2.TEEER
metric is applicable for broadband, networks, andcustomer-premise
equipments.
4. Energyefcient Link Adaptation
The link adaptation is a fundamental procedure that isrelated to
adaptive resource scheduling [17]. It is usedto adapt the link
properties such as modulation andcoding scheme (MCS), and MIMO rank
and precodingaccording to the channel state. The resource
scheduler
will then select a user with good channel gain, and deter-mine
the required number of RBs for this user at a giventransmission
time interval. Thus, the required numberof RBs for each user can be
determined according to theused modulation order, level of
transmitted power, and/or the number of transmitting antennas.
Both, resourcescheduling and link adaptation rely upon the
availablechannel state information at the eNodeB. However,the
adaptation in both link properties and resourcescheduling is
crucial for 3GPP LTE cellular systems to
maximize the system performances. In this section, sev-eral
energyefcient link adaptation techniques such asAMC, power control,
and adaptive transceiving antennaare discussed.
4.1 Adaptive Modulation and Coding
The most appealing feature of AMC is that it can adjustthe
transmission data rate and energy efciency dynami-cally according
to the channel condition. It is well-knownthat the low order
modulation scheme is robust againsthigher level of interference;
however, it provides lowerbit rate. Therefore, low-order modulation
is recom-
mended when signal-to-interference-noise ratio (SINR)is low.
Conversely, when the SINR is relatively high, thehigh modulation
order will be the suitable candidate. TheChannel Quality Indicator
(CQI) plays an important rolein determining the channel quality,
and thus, the codingand modulation level can be recognized
accordingly. InLTE cellular systems, it is important to know that
the CQIreported by the UE is not a SINR direct indicator, but
a4-bit integer which shows the highest MCS that can bedecoded by
the user with a block error rate (BLER) notmore than 10% [18].
Figure 4:Power ow in the base station.
Table 2: TEEER formulas
Equipment type TEEER formula
Soft Switch -log(PTotal / BHCA)
Media Gateway -log(PTotal / Throughput)
Video Multiplexer -log(PTotal / BHCA)
Access (Acess Lines / PTotal ) + 1
Power (POut Total / PIn Total) x 10
Power Amplifier (Wireless) (Total RF Output Power / Total
Input
Power ) x 10
Mechanized Distributing Frames -log(PTotal / # of input
connections)
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The type of the receiver and number of antennas shouldbe taken
into account in the estimation process of the CQIvalue. By using
this procedure, the UE can help the eNo-deB in choosing the
suitable MCS for data transmission.The LTE link level simulator
presented by [19] shows theBLER-SINR curves which give the BLER
value as shownin Figure 5. According to these curves, the MCS can
be
chosen adaptively to maintain the BLER lower than 10%as shown in
Figure 6.
Most of the AMC techniques in the literature havebeen proposed
to maximize the spectral efficiency.However, changing the
constellation size has alsobeen used to maximize the energy
efficiency. In [20],the authors proposed a modulation scaling for
savingenergy. They proposed an energy aware packet sched-uling
system, and emphasized the analogy betweenthe modulation scaling
and voltage scaling. Theyproved that the modulation scaling
exhibits benefitssimilar to that of voltage scaling, and, to some
extent,
it outperforms the voltage scaling in energy-awaresystems.
In addition to change of the constellation size, the authorsin
[21] have ensured that the transmission time and thecircuit energy
should be taken into account in the energyconsumption analysis.
They have examined the MQAMand minimum frequency shift keying
(MFSK) under thedelay and peak-power constraints. It has been
shownthat there will be an energy saving of up to 80% whenthe
transmission time is optimized, especially in shortdistance
transmission.
Furthermore, coding in MQAM and MFSK is also exam-ined in [21].
Trellis-coded modulation with MQAM hasbeen studied and it
outperformed the uncoded MQAM.However, in MFSK, coding can only
reduce the con-sumed energy in large distance transmission.
For orthogonal frequency division multipleaccess (OFDMA)-based
wireless networks, the authorsin [12] and [22] have also proved
that the adaptivemodulation can help to optimize the energy
efciency.Although both works have considered the circuit
powerconsumption in their analysis, different circuit power
model has been adopted by each of them. In [12], theauthors
considered that the total power consumed bythe base station is the
transmitted power plus the circuitpower consumption, without taking
into account theirrelation to the used bandwidth. However, the
authorsin [22] have adopted the following circuit power model:
P W p p Pt tr ct
stat
= +( ) + , (5)
where, Wrepresents the bandwidth used for transmis-sion,
andp
trand pc
t are the transmitted and circuit power
consumed for signal processing per unit bandwidth,respectively,
which are both proportional to the transmis-sion bandwidth.psta
t is the static power consumption thatdoes not have any dened
relation to the transmissionbandwidth, i.e. the power supply and
cooling systems.The latter model seems closer to the practical
situation,and, by considering it in their analysis, the authors
foundthat the modulation order should be adapted accordingto the
channel condition to maximize the energy ef-ciency. As shown in
Figure 7, it is clear that the systemwith adaptive modulation can
transmit higher numberof bits per Joule compared to other systems
with xedmodulation order.
4.2 Power Control
Beside AMC, energyefcient link adaptation can beobtained by
adjusting other transmission parameterssuch as the transmitted
power. Controlling the levelof transmitted power can maximize the
spectral ef-ciency [23,24], and at the same time can manage
theintra-cell and inter-cell interference [25,26]. Thus,energy
efciency can also be optimized in the cellularnetworks by using
energyefcient power control tech-
Figure 5: BLER curves in SISO AWGN simulations for all 15
CQI values. From CQI 1 (leftmost) to CQI 15 (rightmost).
Figure 6: BLER-SNR curves for 1.4MHz with corresponding
MCSs.
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niques [27-29]. In [27] and [28], the game theory wasproposed to
solve the power optimization problemwhich aimed to maximize the
number of bits per joulein a single-carrier, non-cooperative game
scenario. Fora multicarrier scenario, the authors in [29] modeleda
non-cooperative game scenario whereby each userdecides how much
power can be transmitted over
each carrier to maximize the energy efciency. Moredetails about
other game-theoretic approaches used forenergyefcient power control
can be found in [30].
The works shown previously optimize either the spectralefciency
or energy efciency apart from showing thetrade-off between them.
Nevertheless, the authors in [31]address this tradeoff by
developing energyefcientpower optimization for a multi-cell
interference limitedenvironment. In order to understand this
trade-off, theinterference level can be dened as follows:
a=g
g
, (6)
where, is the interference coefcient,gis the channelgain per
user, andg is the interference channel gain. Theincrease in may
represent higher interfering scenario.Then, the energy efciency
over the entire network willbecome as [31]
u p
w p g
pg
p p
t
i i n
t cn
N
( )
log
,,=
++
+
=
12
1
d
=
+( ) +( )
+
Nw p
N p g
p p
t
t
t c
log
,
11 2
(7)
and the network spectral efciency will become as
r p Np
N p g
t
t( ) log ,= + ( ) +( )
1 1 2
(8)
where, Nis the number of users, d 2represents the aver-age noise
power per a block of subcarriers assigned touser n.As shown in
Figure 8,the energy efciency ismore sensitive to power optimization
than the spectralefciency, i.e. for >0 scenario, any increase in
the levelof transmitted power beyond the energyefcient opti-mal
point will signicantly hurt the energy efciencywhile it slightly
improves the spectral efciency.
4.3 Adaptive Transceiving Antenna
MIMO is a well-known strategy which can be used toincrease the
spectral efciency of wireless systems. In3GPP LTE, both an
alamouti-based Space-FrequencyBlock Coding (SFBC) and spatial
multiplexing (SM) areproposed [32].
To further improve the cell edge throughput and thecoverage,
coordinated multipoint (CoMP) has been pro-posed in 3GPP
LTE-Advanced [33]. Although MIMOtechniques showed a signicant
improvement in spec-
Figure 7:The energy efciency performance versus the transmit
power for different modulation schemes [22].
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tral efciency, energy efciency can also be increased.MIMO can
enhance both the spectral and energyefciencies by providing
diversity and SM gains. Thisassumption is true if the circuit power
consumption isnot considered in the calculation of energy
efciency.In other words, MIMO systems are not always
moreenergy-efficient than SISO systems. Therefore, theauthors in
[34] have discussed the trade-off between
the circuit power consumption and the transmissionpower in terms
of energy required per bit. It is shownthat the MIMO systems are
less energyefcient thanSISO for short ranges unless the adaptive
modula-tion is used to balance the circuit and transmit
powerconsumption. In [35], a number of MIMO precodingtechniques,
which can be potentially applied to LTE,are examined in terms of
their combined spectral andpowersaving efciency. These techniques
are SFBC,Random Beamforming, Layered Random Beamform-ing, SU-MIMO,
and MU-MIMO. The authors proposeda cost metric which is the
aggregated power required
for achieving a specic spectral efciency, and theyproved that
MUMIMO is the most powerefcientscheme. In addition to that, MU-MIMO
is preferred inlow mobility scenarios as the inter-user
interference issmall as shown in Figure 9. When the moving speedis
high, on the other hand, the inter-user interferencewith MU-MIMO
becomes more tangible, and therefore,the SU-MIMO will be the
suitable scheme as shown inFigure 10. According to these facts, the
authors in [36]proposed an adaptive switching technique to
switchbetween the transmitting antenna modes according
to the speed (value of interference) and the distancefrom base
station. This switching technique proved asignicant improvement in
energy efciency over theentire system. However, the energy efciency
for MIMOchannels is fully analyzed in [37-39].
Although there was an extensive research on energyefcient link
adaptation schemes, there are still more
issues need to be considered to improve the energy ef-ciency.
First, a near-exact power consumption modelingneeds to be
constructed for different network scenarios.Accordingly, the
optimal energy efcient link propertiescan be obtained. For
energyefcient MIMO schemes,utilizing the spatial resources to
maximize the energyefciency and to mitigate the interference in a
multicellenvironment is still an open issue. Furthermore,
theclosed-loop MIMO schemes were also proposed toenhance the
spectral efciency. However, the enhance-ment of closed-loop over
open-loop SM MIMO schemeson the energy efciency needs more
investigation.
5. Energyefcient Resource Allocation
Due to the high data rate requirements, OFDMA isproposed to
represent the physical layer of LTE cellularsystems. The bandwidth
and power resources shouldbe allocated to the users according to
the designed sys-tem requirements. Most of the resource
optimizationproblems that have been covered in the literature areto
utilize the system bandwidth efciently in order tomaximize the sum
of data rate capacity, which is knownas rate adaptive [40-50].
Another resource optimiza-
Figure 8:Tradeoff of energy efciency and spectral efciency with
different interfering scenarios [31].
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tion problem is known as margin adaptive, whichaims to achieve
the minimum power consumption thatguarantees the QoS requirements
for all users [51-58].A comprehensive overview of rate-adaptive and
mar-gin-adaptive for OFDMA resource allocation has beencovered in
[59,60].
However, the orthogonal frequency division multiplex-ing (OFDM)
system, which is a basic element in OFDMA,addresses a big challenge
from power consumptionpoint of view. It requires RF power amplier
with highpeak-to-average-power ratio as well as the complex
elec-tronic components including the fast Fourier transform
Figure 9:Energy efciency in low speed mobility for MIMO
switching mode.
Figure 10:Energy efciency in high speed mobility for MIMO
switching mode.
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and forward error correction which are not energyef-cient.
Therefore, energyefcient resource allocationhas been adopted
recently to accommodate the greenwireless communications
requirements. Energyefcientresource allocation is a process by
which the RBs, orsubcarriers, would be allocated to users such that
thebits transmitted per joule will be maximized over the
entire network as given by
max
log[ ]. [ ]
. [ ]
, [ ]
,
,
p X k
n j n
j nn
N
j
J p k g kx k2 2
11
1+
==
d
+=P p kc nn
N
[ ]
,
1
(9)
s.t.
log[ ]. [ ]
. [ ] , ,,
,2 21
1+
=
p k g k x k R j
n j n
n
N
j n jreq
d
x k nj nj
J
, [ ] , ,
= 11
where,Jis the number of users, Nis the number of RBs,d
2represents the average noise power per RB,p
nis the
amount of transmitted power, and gn is the average
channel gain. The rst constraint (10) guarantees the
QoSrequirements, while the second constraint (11) assuresthat the
RB would be allocated to one user exclusively.
The initial work in this area has been done by authorsin
[12,61]. The authors proposed an energyefcientresource scheduling
algorithm for at and selectivefading OFDM channels. According to
(9), the optimal
energy efficiency (OptEE) is obtained by using anenergyefcient
scheduler as shown in Figure 11. TheOptEE has proposed that each
user adapts the modula-tion order according to its channel
condition by usingAMC. Then, according to the used MCS, the
numberof RBs is assigned to each user. Each user, however,
can choose the best RB which maximizes the energyefciency over
the entire system. Therefore, the OptEEscheduler achieves the best
energy efciency comparedto rate adaptive scheduler with the xed
transmittedpower of 33 dbm as shown in Figure 11a.
Although this approach can maximize the energy ef-
ciency, it is not fair for users whose channel gain is low.In
other words, the users with good channel gain will con-sume most of
the RBs available greedily. Therefore, theproportional fairness
alongside with the energyefcienttransmission (PropEE) algorithm has
been applied toachieve fair resource allocation among users while
achiev-ing nearoptimal energy efciency [12]. While the
energyefciency can be optimized by using an energyefcientresource
scheduling, there will be a certain degradationin data throughput
as shown in Figure 11b. Therefore, atradeoff between the energy
efciency and throughputshould be dened according to the required
QoS [62].
A latest work that considered the trade-off betweenenergy
efficiency and fairness for OFDMA systemsis proposed by the authors
in [63]. The authors haveformulated the energyefcient resource
allocation byusing game theory optimization. Normally, the
resourceallocation game considers the users as the players ofthe
game. Each user can select the transmit power strat-egy due to his
observation. Once the RB is includedin the allocation process, the
user is very unlikely tochoose the best RB due to incongruity with
the exclu-sive RB allocation. Therefore, by considering the RBsas
non-cooperative players, the authors in [63] usedenergyefcient
correlated equilibrium (CE) to help the
RBs to choose the most satisfying users. In other words,the CE
is achieved when no user would want to deviatefrom the recommended
RB. In order to implement theCE in [63], a linear programming
optimization and adistributed algorithm based on the
regret-matching pro-cedure is used. Although this technique
addresses high
Figure 11:Comparison between OptEE, PropEE, and RA
schedulers.
(a) (b)
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complexity compared to the energyefcient schedulerwith
proportional fairness, it is a plausible concept whichensures
Pareto optimality and fairness for any numberof RBs and users.
Bandwidth Expansion Mode (BEM) is another approachthat has been
proposed in the design of energyefcient
resource allocation for LTE systems [64]. This approachcan be
used to save energy when the network is notfully loaded, whereby
more RBs are not utilized by anyuser. In this case, the authors in
[64] have suggested totrade-off the non-utilized bandwidth for the
transmittedpower by allocating more RBs to the users and switch-ing
back to lower modulation index, and hence lowertransmitted power
can be obtained. By consideringthe advantage of link adaptation,
multi-user diversity,and allocation of spare spectrum, this
algorithm hasshowed 79 to 86% energy reduction over a
conventionalnon-energy aware scheduler throughout the day.
Beside
AMC, antenna adaptation is also examined with BEMalgorithm by
[64]. It has been revealed that MIMO withlow modulation order is
more energyefcient than SISOwith high modulation order. However,
SISO is preferredfor LTE when low spectral efciency is
required.
While more signaling overhead is required by BEM, thetime
compression mode (TCoM) that is complementaryto BEM is another
resource allocation approach proposedby authors in [65] to reduce
the consumed energy by thesignaling overhead. It allows the
scheduler to reduce the
number of allocated RBs to a user to save energy whenthe energy
consumption is dominated by the signalingoverhead. In this case,
the authors in [65] proposed anenergyefcient scorebased scheduler
alongside withBEM and TCoM that should work together to reduce
theenergy consumption, while not compromising the fairnessand data
throughput. By using TCoM, the underutilized
RBs are grouped together and turned off to conserveenergy that
would otherwise be wasted in control chan-nel transmissions. The
fully utilized RBs, on the otherhand, are grouped together and a
higher modulationorder is used. A signicant energy saving of 38%
has beenachieved by combining TCoM with BEM and EESBS com-pared to
the frequency selective proportional fair whichis proposed by [66].
A comprehensive summary for theaforementioned resource schedulers
is shown in Table 3.
Although some researches on energyefcient resourceallocation
have been done, there are many trends and
challenges that need more exploration. For the timebeing, the
energyefcient RB allocation should considerthe interference
management and handoff strategiesin a multi-cell environment, i.e.
reducing transmittedpower would save more energy and reduce the
interfer-ence while sacricing the celledge user
performances.Moreover, the relay-cooperative cellular networks
canalso enhance the energy efciency in addition to increas-ing in
coverage area. However, the energyefcient jointoptimization of RBs
along with the cooperative relaysis still not cleared.
Nevertheless, the resource allocation
Table 3: Energy-efficient resource schedulers, comparison
Scheduler OptEE PropEE BEM TCoM EECE
Author (s) Miaoet al.- 2008 [12,61] Miaoet al-2008 [12,61] Hanet
al. -2011 [64] Videvet al. -2012 [65] Wuet al. -2012 [63]
Objective To optimize the overall
bits transmitted per Joule
of energy in a network
To optimize both energy
efficiency and fairness in
a network
To maximize the EE with
guaranteed QoS
To optimize energy
efficiency, throughput
and fairness
To balance the tradeoff
between the total
energy efficiency and
the fairness
Solution Energy-efficient resource
scheduler
Energy-efficient scheduler
with fairness
Power efficient link adaptation,
exploitation of multi-user
diversity and trading BW for
energy efficiency
Trading BW for
energy efficiency,
controlling the
overhead signals to
save energy
Energy-efficient
resource allocation
scheme by using
the correlated
equilibrium (CE)
Methodology Sorting-Search
algorithm, and link
adaptation (AMC)
Combined sorting-search
algorithm, AMC, and
proportional fairness
Allocating the
spare (non-utilized) RBs, and
switch to lower MCS
bandwidth expanded
mode (BEM), and
Time compression
mode (TCoM)
Game theory, linear
programming method
and a distributed
algorithm based on
the regret matching
procedure
Trade-off Energy efficiency vs.
Throughput (bpJ- bps)
Energy efficiency vs.
Throughput (bpJ- bps)
Power vs. Bandwidth Power vs. bandwidth,
Energy efficiency vs.
overhead
Energy efficiency vs.
fairness
Enhancement Highest energy efficiency
can be obtained compared
to PropEE, Round Robin
energy-efficient scheduler
RREE
Better energy efficiency
than round robin
energy-efficient scheduling
and fairness guaranteed
among users
Significant energy saving (up
to 86%) over a conventional
non-energy aware scheduler
without losses in throughput
when the network is not fully
loaded
Energy saving of
38% over a frequency
selective proportional
fair (FsPF)
Good convergence,
Pareto optimality and
fairness
AMC Adaptive modulation and coding; MCS Modulation and coding
scheme; PropEE Energy efficient with proportional fairness; RREE
Round robin
energy efficient scheduler; EE Energy efficiency; QoS Quality of
service; BW Bandwidth; RBs Resource blocks
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267IETE TECHNICAL REVIEW | VOL 30 | ISSUE 3 | MAY-JUN 2013
process would affect the end-to-end service delay, andhence,
another possible trade-off between the energyefciency and service
delay is addressed as discussedin [67,68].
6. Conclusion and Suggestions forFuture Work
There is an imperative need to improve the energy ef-ciency in
the overall communication networks due tothe negative impact of
emitted CO
2on the environment.
The radio transmission is among the main contributorsof energy
consumption inside the cellular systems. Inthis paper, the
optimization of radio transmission isaddressed by using the
energyefcient approaches, suchas link adaptation and resource
allocation. In link adapta-tion, we outlined the methodologies used
in the literatureto maximize the energy efciency, such as AMC,
MIMO,and power control. Beside the link adaptation, the RRMis
addressed. It shows a considerable improvement in
system performance when it works together with linkadaptation.
Furthermore, other system performances,such as the spectral
efciency and QoS, are investigatedalongside with the energy
efciency by considering thetrade-off between them. Nevertheless,
many challengesstill exist and require more investigation. For
instance,the effect of intercell interference on energy efciencyin
a MIMO-OFDM multi-cell scenario needs furtherstudy. Besides, the
energyefcient resource allocationwith cooperative relay has not
been considered in mostof the previous work. The OFDM-relay
technology isproposed for LTE-Advanced cellular systems to
improvethe cell-edge throughput. Therefore, the number of
relays
along with the other radio resources should be optimizedto
maximize the energy efciency as well as improvingcell coverage
area.
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AUTHORS
Mustafa Ismael Salman received the B.Sc. degree
in electronic and communications engineering from
Al-Nahrain University, Iraq, 2000. Then, he received
the M.Sc. in modern communications from Al-Nahrain
University, Iraq, 2003. Since 2003, he is working as a
senior lecturer in University of Baghdad, teaching many
subjects in the field of computer and communications
engineering. He is currently a PhD student in Wireless
CommunicationsEngineering at Universiti Putra Malaysia. His main
research interests are
green cellular networks, 3GPP LTE networks, OFDM networks, and
adaptive
resource allocation in cellular networks.
E-mail:[email protected]
Muntadher Qasim Abdulhasan received the B.Sc.
degree in Electrical Engineering from Baghdad
University, Iraq, in 2007. He is currently an M.Sc
student in Wireless Communication Engineering at
Universiti Putra Malaysia. His main research interests
are in wireless communication system design with main
emphasis on multiple input multiple output (MIMO)
system and orthogonal frequency division multiplexing (OFDM),
and powerefficiency.
E-mail:[email protected]
Chee Kyun Ng received his Bachelor of Engineering
and Master of Science degrees majoring in Computer
and Communication Systems from Universiti Putra
Malaysia, Serdang, Selangor, Malaysia, in 1999 and2002,
respectively. He has also completed his PhD
program in 2007 majoring in Communications andNetwork
Engineering at the same university. He is
currently undertaking his research on wireless multiple access
schemes,
wireless sensor networks, and smart antenna system. His research
interests
include mobile cellular and satellite communications, digital
signal
processing, and network security. Along the period of his study
programs,
he has published over 100 papers in journals and in
conferences.
E-mail: [email protected]
Nor Kamariah Noordin received her BSc in Electrical
Engineering majoring in Telecommunications from
University of Alabama, USA, in 1987. She became a
tutor at the Department of Computer and Electronics
Engineering, Universiti Putra Malaysia, and pursued
her Masters Degree at Universiti Teknologi Malaysia
and PhD at Universiti Putra Malaysia. She thenbecame a lecturer
in 1991 at the same department where she was later
appointed as the Head from year 2000 to 2002. She is currently
the Deputy
Dean (Academic, Student Affairs and Alumni) of the Faculty.
During her more
than 15 years at the department, she has been actively involved
in teaching,
research, and administrative activities. She has supervised a
number of
undergraduate students as well as postgraduate students in the
area of
wireless communications, which led to receiving some national
and UPM
research awards. Her research work also led her to publish more
than 100
papers in journals and in conferences.
E-mail:[email protected]
Aduwati Sali is currently a Lecturer at Department
of Computer and Communication Systems, Faculty
of Engineering, Universiti Putra Malaysia (UPM) since
July 2003. She obtained her PhD in Mobile Satellite
Communications form University of Surrey, UK, in
July 2009, her MSc in Communications and Network
Engineering from UPM in April 2002, and her BEng
in Electrical Electronics Engineering (Communications) from
University
of Edinburgh in 1999. She worked as an Assistant Manager with
Telekom
Malaysia Bhd from 1999 until 2000. She is involved with EU-IST
Satellite
Network of Excellence (SatNEx) I and II from 2004 until 2009.
She is the
principle investigator for projects under the funding bodies
like Malaysian
Ministry of Science, Technology and Innovation (MOSTI), Research
University
Grant Scheme (RUGS) UPM, and The Academy of Sciences for the
Developing
World (TWAS-COMSTECH) Joint Grants. Her research interests are
radio resource
management, MAC layer protocols, satellite communications,
wireless sensor
networks, disaster management applications, and 3D video
transmissions.
E-mail:[email protected]
Borhanuddin bin Mohd Ali obtained his BSc (Hons)
Electrical and Electronics Engineering from
Loughborough University in 1979; MSc and PhD from
University of Wales, UK, in 1981 and 1985, respectively.
He became a lecturer at the Faculty of Engineering
UPM in 1985, made a Professor in 2002, and Director
of Institute of Multimedia and Software, 2001-2006. In
1997, he cofounded the national networking testbed project code
named
Teman, and became Chairman of the MYREN Research Community in
2002,
the successor to Teman. His research interest is in Wireless
Communications
and Networks where he published over 80 journal and 200
conference
papers. He is a Senior Member of IEEE and a member of IET and a
Chartered
Engineer, and the present ComSoc Chapter Chair. He is presently
on a 2-yearsecondment term with Mimos as a Principal Researcher,
heading the Wireless
Networks and Protocol Research Lab.
E-mail:[email protected]
DOI: 10.4103/0256-4602.113526; Paper No. TR 527_12; Copyright
2013 by the IETE
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